Myokit: A simple interface to cardiac cellular electrophysiology

Clerx, Michael; Collins, Pieter; Lange, Enno de; Volders, Paul G.A.

doi:10.1016/j.pbiomolbio.2015.12.008

Cited by 107 publications

(95 citation statements)

References 113 publications

(53 reference statements)

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“…Simulations were run using Myokit (17), with tolerance settings for the CVODE solver (18) set to = 10 −8 and = 10 −10 . All codes and data are freely available at https://github.com/CardiacModelling/hERGRapidCharacterisation.…”

Section: Methodsmentioning

confidence: 99%

Rapid characterisation of hERG channel kinetics I: using an automated high-throughput system

Lei

Clerx

Gavaghan

et al. 2019

Preprint

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1 Affiliations removed for initial submission as per guidelines. 6 ABSTRACT Predicting how pharmaceuticals may affect heart rhythm is a crucial step in drug-development, and requires a 7 deep understanding of a compound's action on ion channels. In vitro hERG-channel current recordings are an important step in 8 evaluating the pro-arrhythmic potential of small molecules, and are now routinely performed using automated high-throughput 9 patch clamp platforms. These machines can execute traditional voltage clamp protocols aimed at specific gating processes, 10 but the array of protocols needed to fully characterise a current is typically too long to be applied in a single cell. Shorter 11 high-information protocols have recently been introduced which have this capability, but they are not typically compatible with 12 high-throughput platforms. We present a new high-information 15 s protocol to characterise hERG (Kv11.1) kinetics, suitable for 13 both manual and high-throughput systems. We demonstrate its use on the Nanion SyncroPatch 384PE, a 384 well automated 14 patch clamp platform, by applying it to CHO cells stably expressing hERG1a. From these recordings we construct 124 cell-specific 15 variants/parameterisations of a hERG model at 25 • C. A further 8 independent protocols are run in each cell, and are used to 16 validate the model predictions. We then combine the experimental recordings using a hierarchical Bayesian model, which we 17 use to quantify the uncertainty in the model parameters, and their variability from cell to cell, which we use to suggest reasons 18 for the variability. This study demonstrates a robust method to measure and quantify uncertainty, and shows that it is possible 19 and practical to use high-throughput systems to capture full hERG channel kinetics quantitatively and rapidly. 20 21 We present a method for high-throughput characterisation of hERG potassium channel kinetics, via fitting a mathematical 22 model to results of over one hundred single cell patch clamp measurements collected simultaneously on an automated voltage 23 clamp platform. The automated patch clamp data are used to parameterise a mathematical ion channel model fully, opening a 24 new era of automated and rapid development of mathematical models from quick and cheap experiments. The method also 25 allows ample data for independent validation of the models and enables us to study experimental variability and propose its 26 origins. In future the method can be applied to characterise changes to hERG currents in different conditions, for instance 27 at different temperatures (see Part II of the study) or under mutations or the action of pharmaceuticals; and should be easily 28 adapted to study many other currents. Statement of Significance 30The human Ether-à-go-go-Related Gene (hERG) is of great interest in the field of cardiac safety pharmacology. hERG encodes 31 the pore-forming alpha subunit of the ion channel Kv11.1 which conducts the rapid delayed rectifier potassium current, I Kr 32(1)...

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Section: Methodsmentioning

confidence: 99%

Rapid characterisation of hERG channel kinetics I: using an automated high-throughput system

Lei

Clerx

Gavaghan

et al. 2019

Preprint

Self Cite

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“…Different types of APs when reducing the RR as simulated by myokit based on CellML Clerx et al (2016). In the left Figure, the Luo Rudy model (1991) was simulated: for normal parameters (0% I K block), the standard AP was found, while for a 55% I K block an EAD arises, for 60% block the AP behaves as a damped oscillator around a higher equilibrium, similarly for 70% block, but with a smaller amplitude.…”

Section: Eads At the Single Cell Levelmentioning

confidence: 99%

Spatial Patterns of Excitation at Tissue and Whole Organ Level Due to Early Afterdepolarizations

et al. 2017

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Early after depolarizations (EAD) occur in many pathological conditions, such as congenital or acquired channelopathies, drug induced arrhythmias, and several other situations that are associated with increased arrhythmogenicity. In this paper we present an overview of the relevant computational studies on spatial EAD dynamics in 1D, 2D, and in 3D anatomical models and discuss the relation of EADs to cardiac arrhythmias. We also discuss unsolved problems and highlight new lines of research in this area.

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“…This is the case, for example, with CESE (cese.sourceforge.net), Chaste (Mirams et al, 2013; www.cs.ox.ac.uk/chaste), JSim (Butterworth et al, 2014; physiome.org/jsim), Myokit (Clerx et al, 2014; myokit.org) and VCell (Moraru et al, 2008; vcell.org). …”

Section: Discussionmentioning

confidence: 99%

OpenCOR: a modular and interoperable approach to computational biology

Garny

Hunter

2015

Front. Physiol.

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Computational biologists have been developing standards and formats for nearly two decades, with the aim of easing the description and exchange of experimental data, mathematical models, simulation experiments, etc. One of those efforts is CellML (cellml.org), an XML-based markup language for the encoding of mathematical models. Early CellML-based environments include COR and OpenCell. However, both of those tools have limitations and were eventually replaced with OpenCOR (opencor.ws). OpenCOR is an open source modeling environment that is supported on Windows, Linux and OS X. It relies on a modular approach, which means that all of its features come in the form of plugins. Those plugins can be used to organize, edit, simulate and analyze models encoded in the CellML format. We start with an introduction to CellML and two of its early adopters, which limitations eventually led to the development of OpenCOR. We then go onto describing the general philosophy behind OpenCOR, as well as describing its openness and its development process. Next, we illustrate various aspects of OpenCOR, such as its user interface and some of the plugins that come bundled with it (e.g., its editing and simulation plugins). Finally, we discuss some of the advantages and limitations of OpenCOR before drawing some concluding remarks.

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Myokit: A simple interface to cardiac cellular electrophysiology

Cited by 107 publications

References 113 publications

Rapid characterisation of hERG channel kinetics I: using an automated high-throughput system

Rapid characterisation of hERG channel kinetics I: using an automated high-throughput system

Spatial Patterns of Excitation at Tissue and Whole Organ Level Due to Early Afterdepolarizations

OpenCOR: a modular and interoperable approach to computational biology

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